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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Generalized Linear Models (Formula)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.\n",
    "\n",
    "To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data:"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    }
   },
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   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "star98 = sm.datasets.star98.load_pandas().data\n",
    "formula = \"SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
    "           PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF\"\n",
    "dta = star98[\n",
    "    [\n",
    "        \"NABOVE\",\n",
    "        \"NBELOW\",\n",
    "        \"LOWINC\",\n",
    "        \"PERASIAN\",\n",
    "        \"PERBLACK\",\n",
    "        \"PERHISP\",\n",
    "        \"PCTCHRT\",\n",
    "        \"PCTYRRND\",\n",
    "        \"PERMINTE\",\n",
    "        \"AVYRSEXP\",\n",
    "        \"AVSALK\",\n",
    "        \"PERSPENK\",\n",
    "        \"PTRATIO\",\n",
    "        \"PCTAF\",\n",
    "    ]\n",
    "].copy()\n",
    "endog = dta[\"NABOVE\"] / (dta[\"NABOVE\"] + dta.pop(\"NBELOW\"))\n",
    "del dta[\"NABOVE\"]\n",
    "dta[\"SUCCESS\"] = endog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we fit the GLM model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
    "print(mod1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define a function to operate customized data transformation using the formula framework:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def double_it(x):\n",
    "    return 2 * x\n",
    "\n",
    "\n",
    "formula = \"SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
    "           PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF\"\n",
    "mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
    "print(mod2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "print(mod1.params.iloc[1])\n",
    "print(mod2.params.iloc[1] * 2)"
   ]
  }
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